{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This tutorial demonstrates how to perform a multi-objective neural architecture search (NAS) and Quantization Policy (QP) search on a ResNet50 one-shot weight-sharing super-network [1] using the Intel® Neural Compressor Dynamic NAS (DyNAS) search approach. \n",
    "\n",
    "#### Background\n",
    "Neural architecture search, the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural language processing, has seen rapid growth in the machine learning research community. While there have been many recent advancements in NAS, there is still a significant focus on reducing the computational cost incurred when validating discovered architectures by making search more efficient. Evolutionary algorithms, specifically genetic algorithms, have a history of usage in NAS and continue to gain popularity as a highly efficient way to explore the architecture objective space. In this tutorial, we show how evolutionary algorithms [2] can be paired with lightly trained objective predictors in an iterative cycle to accelerate multi-objective architectural exploration. Specifically, we use a bi-level optimization approach [3] denoted as `dynas`. This technique is ~4x more sample efficient than typical one-shot predictor-based NAS approaches. \n",
    "\n",
    "#### Super-Networks\n",
    "\n",
    "The computational overhead of evaluating DNN architectures during the neural architecture search process can be very costly due to the training and validation cycles. To address the training overhead, novel weight-sharing approaches known as one-shot or super-networks have offered a way to mitigate the training overhead by reducing training times from thousands to a few GPU days. These approaches train a task-specific super-network architecture with a weight-sharing mechanism that allows the sub-networks to be treated as unique individual architectures. This enables sub-network model extraction and validation without a separate training cycle. This tutorial offers pre-trained Once-for-All (OFA) super-networks [1] for the image classification on ImageNet-ilsvrc2012 as well as Transformer Language Translation (based on [6]) for the language translation tasks.\n",
    "\n",
    "#### Methodology\n",
    "\n",
    "The flow of the DyNAS approach (`approach='dynas'`) is shown in the following figure. Currently, three pre-trained super-network options for the image classification task are provided. In the first phase of the search, a small population (`config.dynas.population`) of sub-networks are randomly sampled and evaluated (validation measurement) to provide the initial training set for the inner predictor loop. After the predictors are trained, a multi-objective evolutionary search (`search_algorithm`) is performed in the predictor objective space. After an extensive search is performed, the best performing sub-network configurations are selected to be the next iteration's validation population. The cycle continues until the search concludes when the user defined evaluation count (`config.dynas.num_evals`) is met. \n",
    "   \n",
    "<br>\n",
    "<div>\n",
    "<img src=\"DyNAS_flow.png\" width=\"750\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip -q install neural_compressor dynast==1.5.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatievely, if you have a local copy of https://github.com/intel/neural-compressor, you can uncomment and run the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# sys.path.insert(0,'<path to neural compressor>')\n",
    "# !pip install -qr <path to neural compressor>/requirements.txt\n",
    "# !pip install -q dynast==1.5.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neural_compressor.conf.config import NASConfig\n",
    "from neural_compressor.experimental.nas import NAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configure NAS Algorithm\n",
    "\n",
    "The `NASConfig` class allows us to define the appropriate parameters for determining how the neural architecture search is performed. Currently, the following multi-objective evolutionary algorithms are supported by the `dynas` approach: \n",
    "* `'nsga2'`\n",
    "* `'age'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = NASConfig(approach='dynas', search_algorithm='nsga2')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Architecture\n",
    "We currently support pre-trained super-networks:\n",
    "\n",
    "1. Once-for-All (OFA) super-networks [4] for the image classification task on ImageNet-ilsvrc2012. In the case where the super-network PyTorch model download fails, you can manually copy the pre-trained models from https://github.com/mit-han-lab/once-for-all and place them in the `.torch/ofa_nets` path.\n",
    "2. Hardware-Aware-Transformers (HAT) supernetwork [6] for language translation task on WMT14 En-De. To run this supernetwork you have to manually download preprocessed dataset from https://github.com/mit-han-lab/hardware-aware-transformers/blob/master/configs/wmt14.en-de/get_preprocessed.sh and pretrained model from https://www.dropbox.com/s/pkdddxvvpw9a4vq/HAT_wmt14ende_super_space0.pt?dl=0\n",
    "3. BERT Base supernetwork finetuned on the [Stanford Sentiment Treebank](https://huggingface.co/datasets/sst2) dataset. You can download the model from [here](https://github.com/IntelLabs/DyNAS-T/blob/bert/model/models/fine_tuned_bert_sst2.pt) and the script to prepare the SST-2 dataset is available [here](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh#L21).\n",
    "\n",
    "Super-network options (choose 1): \n",
    "- `ofa_resnet50` - based on the ResNet50 architecture [4]. Search space of ~$10^{15}$ architectures.\n",
    "- `inc_quantization_ofa_resnet50` - based on the ResNet50 architecture [4]. This search is performed on `ofa_resnet50` FP32 super-network and is extended with a Quantization Policy search.\n",
    "- `ofa_mbv3_d234_e346_k357_w1.0` - based on the MobileNetV3 architecture [5], width multiplier 1.0. Search space of ~$10^{19}$ architectures.\n",
    "- `ofa_mbv3_d234_e346_k357_w1.2` - based on the MobileNetV3 architecture [5], width multiplier 1.2. Search space of ~$10^{19}$ architectures.  \n",
    "- `transformer_lt_wmt_en_de` - based on the Transformer architecture [7].\n",
    "- `bert_base_sst2` - based on the BERT Base architecture, fine-tuned on the [Stanford Sentiment Treebank](https://huggingface.co/datasets/sst2) dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.dynas.supernet = 'inc_quantization_ofa_resnet50'\n",
    "config.nas.search.seed = 42"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select performance metrics\n",
    "\n",
    "Performance metric options are as follows. You can use from 1 to 3 performance objectives to guide the search.\n",
    "\n",
    "Description:\n",
    "* `'accuracy_top1' or 'bleu' or 'accuracy_sst2'` - ImageNet Top-1 Accuracy (%) (for OFA supetnetworks) and Bleu (for Transformer LT) and accuracy on the SST2 dataset.\n",
    "* `'macs'` - Multiply-and-accumulates as measured from FVCore. \n",
    "* `'latency'` - Latency (inference time) measurement (ms).\n",
    "* `'params'` - Number of parameters in the model.\n",
    "* `'model_size'` - Size of the model in MB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.dynas.metrics = ['accuracy_top1', 'latency']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search parameters\n",
    "\n",
    "* `config.dynas.population` - Size of the population for evolutionary/genetic algorithm (50 recommended)\n",
    "* `config.dynas.num_evals` - Validation measurement count, a higher count comes with greater computational cost but a higher chance of finding optimal sub-networks\n",
    "* `config.dynas.results_csv_path` - Location of the search (validation measurement) results. This file is also used to provide training data to the metric predictors. \n",
    "* `config.dynas.batch_size` - Batch size used during latency measurements.\n",
    "* `config.dynas.eval_batch_size` - Batch size used during accuracy measurements.\n",
    "* `config.dynas.dataset_path` - For OFA it's a path to the imagenet-ilsvrc2012 dataset. This can be obtained at: https://www.image-net.org/download.php; For Transformer LT it's a path to preprocessed WMT EnDe directory (`(...)/data/binary/wmt16_en_de`)\n",
    "* `config.dynas.supernet_ckpt_path` - Transformer LT only. Path to downloaded pretrained super-network (`HAT_wmt14ende_super_space0.pt` file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.dynas.population = 50\n",
    "config.dynas.num_evals = 250\n",
    "config.dynas.results_csv_path = 'search_results.csv'\n",
    "config.dynas.batch_size = 64\n",
    "config.dynas.eval_batch_size = 64\n",
    "config.dynas.dataset_path = '/localdisk/dataset/imagenet' #example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform Search\n",
    "\n",
    "After the DyNAS configuration parameters are set, the search process can be started. Depending on how many evaluations `config.dynas.num_evals` were defined, the search time can vary from hours to days. \n",
    "The search process will populate the `config.dynas.results_csv_path` file and will also return a list of the final iteration's best sub-network population recommondation. \n",
    "\n",
    "Note: example search results are provided for the plotting section if you wish to skip this step for now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = NAS(config)\n",
    "results = agent.search()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Search Results in the Multi-Objective Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import ScalarMappable\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "\n",
    "number_of_evals = 500\n",
    "df_dynas = pd.read_csv('results_resnet50_quant.csv')[:number_of_evals]\n",
    "df_dynas.columns = ['config', 'date', 'params', 'lat', 'model_size', 'top1']\n",
    "\n",
    "cm = plt.cm.get_cmap('viridis_r')\n",
    "count = [x for x in range(len(df_dynas))]\n",
    "\n",
    "ax.scatter(df_dynas['lat'].values, df_dynas['top1'].values, marker='^', alpha=0.8, c=count, \n",
    "           cmap=cm, label='Discovered DNN Model', s=10)\n",
    "ax.set_title(f'Intel® Neural Compressor\\nDynamic NAS (DyNAS)\\nSupernet:{config.dynas.supernet}')\n",
    "ax.set_xlabel('Latency (normalized)', fontsize=13)\n",
    "ax.set_ylabel('Top-1 Accuracy (%)', fontsize=13)\n",
    "ax.legend(fancybox=True, fontsize=10, framealpha=1, borderpad=0.2, loc='lower right')\n",
    "ax.grid(True, alpha=0.3)\n",
    "#ax.set_ylim(72,77.5)\n",
    "\n",
    "# Eval Count bar\n",
    "norm = plt.Normalize(0, len(df_dynas))\n",
    "sm = ScalarMappable(norm=norm, cmap=cm)\n",
    "cbar = fig.colorbar(sm, ax=ax, shrink=0.85)\n",
    "cbar.ax.set_title(\"         Evaluation\\n  Count\", fontsize=8)\n",
    "\n",
    "fig.tight_layout(pad=2)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] Cai, H., Gan, C., & Han, S. (2020). Once for All: Train One Network and Specialize it for Efficient Deployment. ArXiv, abs/1908.09791.   \n",
    "[2] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, \"A fast and elitist multiobjective genetic algorithm: NSGA-II,\" in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, April 2002, doi: 10.1109/4235.996017. \n",
    "[3] Cummings, D., Sarah, A., Sridhar, S.N., Szankin, M., Muñoz, J.P., & Sundaresan, S. (2022). A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities. ArXiv, abs/2205.10358.   \n",
    "[4] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.  \n",
    "[5] Howard, A.G., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q.V., & Adam, H. (2019). Searching for MobileNetV3. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 1314-1324.    \n",
    "[6] Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C. and Han, S., 2020. Hat: Hardware-aware transformers for efficient natural language processing. arXiv preprint arXiv:2005.14187.    \n",
    "[7] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30."
   ]
  }
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